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Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method

Author

Listed:
  • Myungsik Do

    (Department of Urban Engineering, Hanbat National University, Daejeon 34158, Korea)

  • Wanhee Byun

    (Future Strategy Research Center, Land & Housing Institute, Daejeon 34047, Korea)

  • Doh Kyoum Shin

    (DataWiz Ltd., Mokwon University, Daejeon 35349, Korea)

  • Hyeryun Jin

    (Center of Infrastructure Asset Management, Hanbat National University, Daejeon 34158, Korea)

Abstract

It is common to call a taxi by taxi-apps in Korea and it was believed that an app-taxi service would provide customers with more convenience. However, customers’ requests can often be denied, as taxi drivers can decide whether to take calls from customers or not. Therefore, studies on factors that determine whether taxi drivers refuse or accept calls from customers are needed. This study investigated why taxi drivers might refuse calls from customers and factors that influence the success of matching within the service. This study used origin-destination data in Seoul and Daejeon obtained from T-map Taxis, which was analyzed via a decision tree using machine learning. Cross-validation was also performed. Results showed that distance, socio-economic features, and land uses affected matching success rate. Furthermore, distance was the most important factor in both Seoul and Daejeon. The matching success rate in Seoul was lowest for trips shorter than the average at midnight. In Daejeon, the rate was lowest when the calls were made for trips either shorter or longer than the average distance. This study showed that the matching success for ride-hailing services can be differentiated particularly by the distance of the requested trip depending on the size of the city.

Suggested Citation

  • Myungsik Do & Wanhee Byun & Doh Kyoum Shin & Hyeryun Jin, 2019. "Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method," Sustainability, MDPI, vol. 11(20), pages 1-13, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5615-:d:275648
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    References listed on IDEAS

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    1. Changhyo Yi & Kijung Kim, 2018. "A Machine Learning Approach to the Residential Relocation Distance of Households in the Seoul Metropolitan Region," Sustainability, MDPI, vol. 10(9), pages 1-19, August.
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    Cited by:

    1. JinHyo Joseph Yun & Xiaofei Zhao & KwangHo Jung & Tan Yigitcanlar, 2020. "The Culture for Open Innovation Dynamics," Sustainability, MDPI, vol. 12(12), pages 1-21, June.
    2. JinHyo Joseph Yun & Xiaofei Zhao & Jinxi Wu & John C. Yi & KyungBae Park & WooYoung Jung, 2020. "Business Model, Open Innovation, and Sustainability in Car Sharing Industry—Comparing Three Economies," Sustainability, MDPI, vol. 12(5), pages 1-27, March.
    3. Mohammadbashir Sedighi & Hamideh Parsaeiyan & Yashar Araghi, 2021. "An Empirical Study of Intention to Continue Using of Digital Ride-hailing Platforms," The Review of Socionetwork Strategies, Springer, vol. 15(2), pages 489-515, November.
    4. Chee Sun Lee & Peck Yeng Sharon Cheang & Massoud Moslehpour, 2022. "Predictive Analytics in Business Analytics: Decision Tree," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(1), pages 1-30, March.
    5. Tubagus Robbi Megantara & Sudradjat Supian & Diah Chaerani, 2022. "Strategies to Reduce Ride-Hailing Fuel Consumption Caused by Pick-Up Trips: A Mathematical Model under Uncertainty," Sustainability, MDPI, vol. 14(17), pages 1-18, August.

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